




"""simple docstring"""




import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowerCAmelCase				:     Tuple					=				logging.get_logger(__name__)






class        __magic_name__				(			enum.Enum							):






				'''simple docstring'''
				__UpperCamelCase         =		0
				__UpperCamelCase         =		1






@add_end_docstrings(UpperCAmelCase__							)
class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"generated"





				def __init__(			self						,					*_a						,					**_a					):





							"""simple docstring"""
							super().__init__(*_a						,					**_a					)

							self.check_model_type(
							    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							    if self.framework == """tf"""
							    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING					)





				def   _lowerCAmelCase							(			self						,					_a=None						,					_a=None						,					_a=None						,					_a=None						,					_a=None						,					_a=None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   {}
							if truncation is not None:
										lowerCamelCase      =   truncation

							lowerCamelCase      =   generate_kwargs

							lowerCamelCase      =   {}
							if return_tensors is not None and return_type is None:
										lowerCamelCase      =   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
							if return_type is not None:
										lowerCamelCase      =   return_type

							if clean_up_tokenization_spaces is not None:
										lowerCamelCase      =   clean_up_tokenization_spaces

							if stop_sequence is not None:
										lowerCamelCase      =   self.tokenizer.encode(_a						,					add_special_tokens=_a					)
										if len(_a					) > 1:
													warnings.warn(
													    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
													    """ the stop sequence will be used as the stop sequence string in the interim."""					)
										lowerCamelCase      =   stop_sequence_ids[0]

							return preprocess_params, forward_params, postprocess_params





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							return True





				def   _lowerCAmelCase							(			self						,					*_a						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   self.model.config.prefix if self.model.config.prefix is not None else """"""
							if isinstance(args[0]						,					_a					):
										if self.tokenizer.pad_token_id is None:
													raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""					)
										lowerCamelCase      =   ([prefix + arg for arg in args[0]],)
										lowerCamelCase      =   True

							elif isinstance(args[0]						,					_a					):
										lowerCamelCase      =   (prefix + args[0],)
										lowerCamelCase      =   False
							else:
										raise ValueError(
										    f' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'					)
							lowerCamelCase      =   self.tokenizer(*_a						,					padding=_a						,					truncation=_a						,					return_tensors=self.framework					)
							# This is produced by tokenizers but is an invalid generate kwargs
							if "token_type_ids" in inputs:
										del inputs["token_type_ids"]
							return inputs





				def __call__(			self						,					*_a						,					**_a					):





							"""simple docstring"""
							lowerCamelCase      =   super().__call__(*_a						,					**_a					)
							if (
							    isinstance(args[0]						,					_a					)
							    and all(isinstance(_a						,					_a					) for el in args[0]					)
							    and all(len(_a					) == 1 for res in result					)
							):
										return [res[0] for res in result]
							return result





				def   _lowerCAmelCase							(			self						,					_a						,					_a=TruncationStrategy.DO_NOT_TRUNCATE						,					**_a					):





							"""simple docstring"""
							lowerCamelCase      =   self._parse_and_tokenize(_a						,					truncation=_a						,					**_a					)
							return inputs





				def   _lowerCAmelCase							(			self						,					_a						,					**_a					):





							"""simple docstring"""
							if self.framework == "pt":
										lowerCamelCase ,  lowerCamelCase      =   model_inputs["""input_ids"""].shape
							elif self.framework == "tf":
										lowerCamelCase ,  lowerCamelCase      =   tf.shape(model_inputs["""input_ids"""]					).numpy()

							lowerCamelCase      =   generate_kwargs.get("""min_length"""						,					self.model.config.min_length					)
							lowerCamelCase      =   generate_kwargs.get("""max_length"""						,					self.model.config.max_length					)
							self.check_inputs(_a						,					generate_kwargs["""min_length"""]						,					generate_kwargs["""max_length"""]					)
							lowerCamelCase      =   self.model.generate(**_a						,					**_a					)
							lowerCamelCase      =   output_ids.shape[0]
							if self.framework == "pt":
										lowerCamelCase      =   output_ids.reshape(_a						,					out_b // in_b						,					*output_ids.shape[1:]					)
							elif self.framework == "tf":
										lowerCamelCase      =   tf.reshape(_a						,					(in_b, out_b // in_b, *output_ids.shape[1:])					)
							return {"output_ids": output_ids}





				def   _lowerCAmelCase							(			self						,					_a						,					_a=ReturnType.TEXT						,					_a=False					):





							"""simple docstring"""
							lowerCamelCase      =   []
							for output_ids in model_outputs["output_ids"][0]:
										if return_type == ReturnType.TENSORS:
													lowerCamelCase      =   {f'{self.return_name}_token_ids': output_ids}
										elif return_type == ReturnType.TEXT:
													lowerCamelCase      =   {
													    f'{self.return_name}_text': self.tokenizer.decode(
													        _a						,					skip_special_tokens=_a						,					clean_up_tokenization_spaces=_a						,					)
													}
										records.append(_a					)
							return records






@add_end_docstrings(UpperCAmelCase__							)
class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"summary"





				def __call__(			self						,					*_a						,					**_a					):





							"""simple docstring"""
							return super().__call__(*_a						,					**_a					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							if max_length < min_length:
										logger.warning(f'Your min_length={min_length} must be inferior than your max_length={max_length}.'					)

							if input_length < max_length:
										logger.warning(
										    f'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
										    """a summarization task, where outputs shorter than the input are typically wanted, you might """
										    f'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'					)






@add_end_docstrings(UpperCAmelCase__							)
class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		"translation"





				def   _lowerCAmelCase							(			self						,					_a						,					_a						,					_a					):





							"""simple docstring"""
							if input_length > 0.9 * max_length:
										logger.warning(
										    f'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
										    """increasing your max_length manually, e.g. translator('...', max_length=400)"""					)
							return True





				def   _lowerCAmelCase							(			self						,					*_a						,					_a=TruncationStrategy.DO_NOT_TRUNCATE						,					_a=None						,					_a=None					):





							"""simple docstring"""
							if getattr(self.tokenizer						,					"""_build_translation_inputs"""						,					_a					):
										return self.tokenizer._build_translation_inputs(
										    *_a						,					return_tensors=self.framework						,					truncation=_a						,					src_lang=_a						,					tgt_lang=_a					)
							else:
										return super()._parse_and_tokenize(*_a						,					truncation=_a					)





				def   _lowerCAmelCase							(			self						,					_a=None						,					_a=None						,					**_a					):





							"""simple docstring"""
							lowerCamelCase ,  lowerCamelCase ,  lowerCamelCase      =   super()._sanitize_parameters(**_a					)
							if src_lang is not None:
										lowerCamelCase      =   src_lang
							if tgt_lang is not None:
										lowerCamelCase      =   tgt_lang
							if src_lang is None and tgt_lang is None:
										# Backward compatibility, direct arguments use is preferred.
										lowerCamelCase      =   kwargs.get("""task"""						,					self.task					)
										lowerCamelCase      =   task.split("""_"""					)
										if task and len(_a					) == 4:
													# translation, XX, to YY
													lowerCamelCase      =   items[1]
													lowerCamelCase      =   items[3]
							return preprocess_params, forward_params, postprocess_params





				def __call__(			self						,					*_a						,					**_a					):





							"""simple docstring"""
							return super().__call__(*_a						,					**_a					)









"""simple docstring"""




import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowerCAmelCase				:     Dict					=				namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def        a__					(							snake_case__			,					snake_case__							)		->   Optional[Any]:
			return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   _TestCommandArgs(dataset=snake_case__			,					all_configs=snake_case__			,					save_infos=snake_case__							)
			lowerCamelCase      =   TestCommand(*snake_case__							)
			test_command.run()
			lowerCamelCase      =   os.path.join(snake_case__			,					"""README.md"""							)
			assert os.path.exists(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict.from_directory(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict(
			    {
			        """default""": DatasetInfo(
			            features=Features(
			                {
			                    """tokens""": Sequence(Value("""string"""							)							),
			                    """ner_tags""": Sequence(
			                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
			                    """langs""": Sequence(Value("""string"""							)							),
			                    """spans""": Sequence(Value("""string"""							)							),
			                }							)			,					splits=[
			                {
			                    """name""": """train""",
			                    """num_bytes""": 2_35_15_63,
			                    """num_examples""": 1_00_00,
			                },
			                {
			                    """name""": """validation""",
			                    """num_bytes""": 23_84_18,
			                    """num_examples""": 10_00,
			                },
			            ]			,					download_size=3_94_06_80			,					dataset_size=2_58_99_81			,					)
			    }							)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase ,  lowerCamelCase      =   getattr(dataset_infos["""default"""]			,					snake_case__							), getattr(expected_dataset_infos["""default"""]			,					snake_case__							)
						if key == "num_bytes":
									assert is_apercent_close(snake_case__			,					snake_case__							)
						elif key == "splits":
									assert list(snake_case__							) == list(snake_case__							)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes			,					expected[split].num_bytes							)
						else:
									result == expected



